Predicting the Performance of MSMEs: A Hybrid DEA-machine Learning Approach

Sabri Boubaker, T.D.Q. Le, Thanh Ngo and R. Manita

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Abstract: Micro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010-2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management. \textcopyright 2023, The Author(s).

Keywords: Common set of weights (CSW); Data envelopment analysis (DEA); Efficiency; Machine learning (ML); Micro; small; and medium enterprise (MSME) (search for similar items in EconPapers)
Date: 2023
Note: View the original document on HAL open archive server: https://normandie-univ.hal.science/hal-04434027v1
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Published in Annals of Operations Research, 2023, ⟨10.1007/s10479-023-05230-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04434027

DOI: 10.1007/s10479-023-05230-8

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